Structural fibres are an effective method to improve concrete post-cracking tensile strength (f_ctR). Currently, the characterization of this property is mainly performed experimentally. This is a source of uncertainties at design stages, which hinders the development of new fibre type and/or optimization of those currently existing. This paper presents a multilayer perceptron neural network to predict f_ctR of fibre-reinforced concrete (FRC) subjected to the Barcelona Test. The optimal architecture of the predictor is obtained by evaluating 9216 configurations of input dimension and number of hidden layers and neurons. The generalization performance is assessed using repeated random sub-sampling validation with 50 iterations. The final model can predict with high accuracy the f_ctR of FRC for different cracking stages. A parametric analysis is performed to prove coherence between the results predicted by the model and the established understanding of the FRC behaviour. Finally, numerical expressions are provided as an alternative tool to traditional testing to predict the residual strength of the Barcelona Test for pre-design and quality control purposes based on fibre dosage, concrete strength, specimen type and height and fibre geometric characteristics. These type of approaches are found to be necessary for boosting the development of the FRC technology.

结构纤维是提高混凝土开裂后抗拉强度（f _ctR）的有效方法。目前，该特性的表征主要通过实验进行。这是设计阶段不确定性的来源，阻碍了新光纤类型的开发和/或现有光纤的优化。本文提出了一种多层感知器神经网络来预测f _ctR经过巴塞罗那测试的纤维增强混凝土 (FRC)。预测器的最优架构是通过评估输入维度的 9216 个配置以及隐藏层和神经元的数量来获得的。使用重复随机子采样验证和 50 次迭代评估泛化性能。最终模型可以高精度地预测f _ctR不同裂解阶段的 FRC。执行参数分析以证明模型预测的结果与对 FRC 行为的既定理解之间的一致性。最后，提供了数值表达式作为传统测试的替代工具，以根据纤维剂量、混凝土强度、试样类型和高度以及纤维几何特性，预测巴塞罗那测试的残余强度，以进行预设计和质量控制。发现这些类型的方法对于促进 FRC 技术的发展是必要的。